Phase patterning of liquid crystal elastomers by laser-induced dynamic crosslinking.

Liquid crystal elastomers hold promise in various fields due to their reversible transition of mechanical and optical properties across distinct phases. However, the lack of local phase patterning techniques and irreversible phase programming has hindered their broad implementation. Here we introduce laser-induced dynamic crosslinking, which leverages the precision and control offered by laser technology to achieve high-resolution multilevel patterning and transmittance modulation. Incorporation of allyl sulfide groups enables adaptive liquid crystal elastomers that can be reconfigured into desired phases or complex patterns. Laser-induced dynamic crosslinking is compatible with existing processing methods and allows the generation of thermo- and strain-responsive patterns that include isotropic, polydomain and monodomain phases within a single liquid crystal elastomer film. We show temporary information encryption at body temperature, expanding the functionality of liquid crystal elastomer devices in wearable applications.

A Gradient Stiffness-Programmed Circuit Board by Spatially Controlled Phase-Transition of Supercooled Hydrogel for Stretchable Electronics Integration.

Due to emerging demands in soft electronics, there is an increasing need for material architectures that support robust interfacing between soft substrates, stretchable electrical interconnects, and embedded rigid microelectronics chips. Though researchers have adopted rigid-island structures to solve the issue, this approach merely shifts stress concentrations from chip-conductor interfaces to rigid-island-soft region interfaces in the substrate. Here, a gradient stiffness-programmed circuit board (GS-PCB) that possesses high stretchability and stability with surface mounted chips is introduced. The board comprises a stiffness-programmed hydrogel substrate and a laser-patterned liquid metal conductor. The hydrogel simultaneously obtains a large stiffness disparity and robust interfaces between rigid-islands and soft regions. These seemingly contradictory conditions are accomplished by adopting a gradient stiffness structure at the interfaces, enabled by combining polymers with different interaction energies and a supercooled sodium acetate solution. By integrating the gel with laser-patterned liquid metal with exceptional properties, GS-PCB exhibits higher electromechanical stability than other rigid-island research. To highlight the practicality of this approach, a finger-sensor device that successfully distinguishes objects by direct physical contact is fabricated, demonstrating its stability under various mechanical disturbances.

Tunable Radiative Cooling by Mechanochromic Electrospun Micro-Nanofiber Matrix.

Radiative thermoregulation has been regarded as an energy-efficient method for thermal management. In this study, the development of a mechanoresponsive polydimethylsiloxane (PDMS) micro-nanofiber matrix capable of both sub-ambient radiative cooling and solar heating is presented, achieved through a core-shell electrospinning technique. The electrospun PDMS micro-nanofibers, with diameters comparable to the solar wavelengths, exhibit excellent solar reflectivity (≈93%) while preserving its pristine high infrared (IR) emissivity. As a result, the electrospun PDMS radiative cooler (EPRC) successfully demonstrated sub-ambient radiative cooling performance (≈3.8°C) during the daytime. Furthermore, the exceptional resilient property of PDMS facilitated the reversible alteration of the structural morphology created by the fiber-based matrix under mechanical force, resulting in the modulation of solar reflectivity (≈80%). The precise modulation of solar reflectivity enabled reversibly switchable multi-step radiative thermoregulation, offering enhanced flexibility in addressing varying thermal environments even in maintaining the desired temperature. The findings of this work offer a promising approach toward dynamic radiative thermoregulation, which holds significant potential for addressing global climate change concerns and energy shortage.

Transparent Electronics for Wearable Electronics Application.

Recent advancements in wearable electronics offer seamless integration with the human body for extracting various biophysical and biochemical information for real-time health monitoring, clinical diagnostics, and augmented reality. Enormous efforts have been dedicated to imparting stretchability/flexibility and softness to electronic devices through materials science and structural modifications that enable stable and comfortable integration of these devices with the curvilinear and soft human body. However, the optical properties of these devices are still in the early stages of consideration. By incorporating transparency, visual information from interfacing biological systems can be preserved and utilized for comprehensive clinical diagnosis with image analysis techniques. Additionally, transparency provides optical imperceptibility, alleviating reluctance to wear the device on exposed skin. This review discusses the recent advancement of transparent wearable electronics in a comprehensive way that includes materials, processing, devices, and applications. Materials for transparent wearable electronics are discussed regarding their characteristics, synthesis, and engineering strategies for property enhancements. We also examine bridging techniques for stable integration with the soft human body. Building blocks for wearable electronic systems, including sensors, energy devices, actuators, and displays, are discussed with their mechanisms and performances. Lastly, we summarize the potential applications and conclude with the remaining challenges and prospects.

Clustering Approach for Detecting Multiple Types of Adversarial Examples.

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture. Especially, defense methods using adversarial example detection architecture have been actively studied. This is because defense methods using adversarial example detection architecture do not make wrong decisions for the legitimate input data while others do. In this paper, we note that current defense methods using adversarial example detection architecture can classify the input data into only either a legitimate one or an adversarial one. That is, the current defense methods using adversarial example detection architecture can only detect the adversarial examples and cannot classify the input data into multiple classes of data, i.e., legitimate input data and various types of adversarial examples. To classify the input data into multiple classes of data while increasing the accuracy of the clustering model, we propose an advanced defense method using adversarial example detection architecture, which extracts the key features from the input data and feeds the extracted features into a clustering model. From the experimental results under various application datasets, we show that the proposed method can detect the adversarial examples while classifying the types of adversarial examples. We also show that the accuracy of the proposed method outperforms the accuracy of recent defense methods using adversarial example detection architecture.

Is Homomorphic Encryption-Based Deep Learning Secure Enough?

As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysis data-is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user's data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years. If so, is it possible to use the deep learning service while preserving the data privacy of users by using the data to which homomorphic encryption is applied? In this paper, we propose three attack methods to infringe user's data privacy by exploiting possible security vulnerabilities in the process of using homomorphic encryption-based deep learning services for the first time. To specify and verify the feasibility of exploiting possible security vulnerabilities, we propose three attacks: (1) an adversarial attack exploiting communication link between client and trusted party; (2) a reconstruction attack using the paired input and output data; and (3) a membership inference attack by malicious insider. In addition, we describe real-world exploit scenarios for financial and medical services. From the experimental evaluation results, we show that the adversarial example and reconstruction attacks are a practical threat to homomorphic encryption-based deep learning models. The adversarial attack decreased average classification accuracy from 0.927 to 0.043, and the reconstruction attack showed average reclassification accuracy of 0.888, respectively.

Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack.

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

Moiré-Free Imperceptible and Flexible Random Metal Grid Electrodes with Large Figure-of-Merit by Photonic Sintering Control of Copper Nanoparticles.

Flexible micro/nano metal grid transparent conductors emerged as an alternative to the fragile/rigid indium tin oxide electrode. They are usually fabricated by the combination of the conventional photolithography and the vacuum deposition of regular metal grid patterns, however, seriously suffer from moiré and starburst problems induced by periodic regular pattern structures. In this paper, we demonstrated flexible and imperceptible random copper microconductors with an extremely high figure-of-merit (∼2000) by the thermal conduction layer-assisted photonic sintering of copper nanoparticles without damages in the plastic substrate. This process can be easily applied to complicated structures and surfaces including a random pattern which is imperceptible and free of interferences. As a proof-of-concept, a transparent windshield defogger in a car was demonstrated with a Cu transparent random conductor at an extreme and reversible fogging state.

Malignant fibrous histiocytoma of the maxillary sinus in a spray painter from an automobile repair shop.

BACKGROUND: We report a case of a spray painter who developed malignant fibrous histiocytoma (MFH) of the maxillary sinus following long-term exposure to chromium, nickel, and formaldehyde, implying that these agents are probable causal agents of MFH. CASE REPORT: The patient developed right-sided prosopalgia that began twenty months ago. The symptom persisted despite medical treatment. After two months, he was diagnosed with MFH through imaging studies, surgery, and pathological microscopic findings at a university hospital in Seoul. His social, medical, and family history was unremarkable.The patient had worked for about 18 years at an automobile repair shop as a spray painter. During this period, he had been exposed to various occupational agents, such as hexavalent chromium, nickel, and formaldehyde, without appropriate personal protective equipment. He painted 6 days a week and worked for about 8 hours a day.Investigation of the patient's work environment detected hexavalent chromium, chromate, nickel, and formaldehyde. CONCLUSIONS: The study revealed that the patient had been exposed to hexavalent chromium, formaldehyde, and nickel compounds through sanding and spray painting. The association between paranasal cancer and exposure to the aforementioned occupational human carcinogens has been established. We suggest, in this case, the possibility that the paint spraying acted as a causal agent for paranasal cancer.

Comparison of clinical effects according to the dosage of sufentanil added to 0.5% hyperbaric bupivacaine for spinal anesthesia in patients undergoing cesarean section.

BACKGROUND: Subarachnoid block is a widely used technique for cesarean section. To improve the quality of analgesia and prolong the duration of analgesia, addition of intrathecal opioids to local anesthetics has been encouraged. We compared the effects of sufentanil 2.5 µg and 5 µg, which were added to intrathecal hyperbaric bupivacaine. METHODS: We enrolled 105 full term parturients were randomly divided into 3 groups: Group 1 (control), Group 2 (sufentanil 2.5 µg), and Group 3 (sufentanil 5 µg). In every group, 0.5% heavy bupivacaine was added according to the adjusted dose regimen. We determined the maximum level of sensory block and motor block, the quality of intraoperative analgesia, the duration of effective analgesia and side effects. RESULTS: There were no significant differences among the 3 groups in the maximum level of the sensory block and motor block. Recovery rate of the sensory block, however, was significantly slower in Group 3 than Group 1. Quality of intraopertive analgesia, muscle relaxation, and duration of effective analgesia were enhanced by increasing the dosage of intrathecal sufentanil. Frequencies of hypotension, maximum sedation level, and pruritus were directly related to the dosage of intrathecal sufentanil, whereas nausea and vomiting occurred only in the groups using sufentanil. CONCLUSIONS: The addition of sufentanil 2.5 µg for spinal anesthesia provides adequate intraoperative analgesia and good postoperative analgesia with minimal adverse effects on the mother.

The optimal effect-site concentration of remifentanil to attenuate the pain caused by propofol.

BACKGROUND: The injection pain of propofol is a frequent and well-known adverse effect. This study was designed to determine the optimal effect-site concentration of remifentanil for minimizing injection pain during induction with propofol. METHODS: A total intravenous anesthetic technique was used for patients undergoing general anesthesia and remifentanil was pretreated to reach a certain target concentration before propofol injection. Using Dixon's up-and-down method, the degree of pain described by the patient was used to adjust the target concentration of remifentanil for the next patient. Ten success-failure curves (crossovers) were sought to find the effect-site concentration (EC) of remifentanil for minimizing injection pain of propofol. RESULTS: The EC of remifentanil in 50% and 95% of adult female population (EC(50) and EC(95)) for minimizing injection pain of propofol were 3.09 ng/ml (95% confidence limits [CI] 2.92-3.30 ng/ml) and 3.78 ng/ml (95% CI 3.45-3.95 ng/ml), respectively. Clinically significant hemodynamic compromise or respiratory complications were not found during remifentanil infusion. CONCLUSIONS: Maintaining 3.78 ng/ml EC of remifentanil during induction with propofol attenuate propofol injection pain without serious adverse events in female patients undergoing general anesthesia and this method may provide the patient's comfort without preparing other drugs for pain relief.

Advantages of robot-assisted laparoscopic radical prostatectomy in obese patients: comparison with the open procedure.

PURPOSE: Obesity has been suggested as a risk factor for worse perioperative outcomes, especially in radical prostatectomy, in several studies. However, the impact of obesity on perioperative outcomes has not yet been well elucidated for robot-assisted laparoscopic radical prostatectomy (RALP). We evaluated whether obesity had an adverse effect on outcomes following RALP compared with retropubic radical prostatectomy (RRP). MATERIALS AND METHODS: From April 2008 to May 2011, 181 patients underwent radical prostatectomy (RALP, 111; RRP, 70). These patients were subdivided into two groups according to body mass index (BMI): the nonobese group (BMI, 25 kg/m(2) or less) and the obese group (BMI, greater than 25 kg/m(2)). Perioperative outcomes in RALP and RRP were retrospectively compared between the two groups. RESULTS: In RRP, patients in the obese group (n=20) showed greater blood loss and a higher complication rate than did those in the nonobese group (n=50). However, in RALP, no statistically significant differences in perioperative outcomes were observed between the obese (n=37) and the nonobese (n=74) groups. RALP showed less blood loss and a lower complication rate in both the obese and nonobese groups than did RRP. CONCLUSIONS: RALP is thought to be a more effective and safer procedure in obese patients compared with traditional open radical prostatectomy. In the management of obese patients with localized prostate cancer, RALP should be considered as a primary choice for treatment.